MR‐UNet: An UNet model using multi‐scale and residual convolutions for retinal vessel segmentation

Abstract The overall performance of the retinal vessel segmentation network based on improved UNet is excellent, but there is still room for improvement in the small blood vessel segmentation. Therefore, this paper proposes an improved MR‐UNet, which designs two new blocks: the multi‐scale convolution (Multiconv) block and the residual convolution (Resconv) block. The Multiconv block uses different size convolution kernels to extract the characteristics of different thicknesses of retinal blood vessels, thereby improving the model's ability to segment small blood vessels. The Resconv block uses different convolutional layers to process the shallow semantic information in the encoding stage and then concatenates it to the decoding stage, reducing the semantic difference between the encoder and the decoder. On the retinal data sets DRIVE, STARE and CHASE_DB1, the accuracy (Acc) of this model is 0.9705, 0.9747, 0.9778, the specificity (Sp) is 0.9863, 0.9892, 0.9930, the AUC is 0.9872, 0.9849, 0.9925, and the F1‐Score is 0.8270, 0.8134, 0.8460, respectively. Compared with the original UNet, Se, Sp, and F1‐Score of MR‐UNet increase by 2.5%, 0.5%, and 0.4%, respectively, proving that MR‐UNet has better comprehensive performance.